A translation-engine suggestion method, system, and computer program product include identifying probes for third-party translation-engines for an input text, segmenting sections of the input text into a plurality of segments according to the identified probes, fragmenting the input text into fragments according to the segments, applying each fragment to the identified probe using the corresponding third-party translation-engine, and outputting a translation by combining each fragment.

A device includes a memory adapted to store a list in a file or database comprising a plurality of vocabulary words in a first language and, for each vocabulary word, a corresponding word in a second language, a display device, and a processor. The processor is adapted to receive a plurality of words in the first language, select one or more words among the plurality of words, based on one or more predetermined criteria, translate, match or equate the one or more selected words from the first language to words of the second language, and cause the display device to display the plurality of words, wherein one or more first words that are in the plurality of words and are not among the one or more selected words which are displayed in the first language and one or more second words that are in the plurality of words and are among the one or more selected words are displayed in the second language.

A device includes a memory adapted to store a list in a file or database comprising a plurality of vocabulary words in a first language and, for each vocabulary word, a corresponding word in a second language, a display device, and a processor. The processor is adapted to receive a plurality of words in the first language, select one or more words among the plurality of words, based on one or more predetermined criteria, translate, match or equate the one or more selected words from the first language to words of the second language, and cause the display device to display the plurality of words, wherein one or more first words that are in the plurality of words and are not among the one or more selected words which are displayed in the first language and one or more second words that are in the plurality of words and are among the one or more selected words are displayed in the second language.

Fragment recall and adaptive automated translation are disclosed herein. An example method includes determining that an exact or fuzzy match for a portion of a source input cannot be found in a translation memory, performing fragment recall by matching subsegments in the portion against one or more whole translation units stored in the translation memory, and matching subsegments in the portion against corresponding one or more subsegments inside the one or more matching whole translation units, and returning any of the one or more matching whole translation units and the one or more matching subsegments as a fuzzy match, as well as the translations of those subsegments.

Fragment recall and adaptive automated translation are disclosed herein. An example method includes determining that an exact or fuzzy match for a portion of a source input cannot be found in a translation memory, performing fragment recall by matching subsegments in the portion against one or more whole translation units stored in the translation memory, and matching subsegments in the portion against corresponding one or more subsegments inside the one or more matching whole translation units, and returning any of the one or more matching whole translation units and the one or more matching subsegments as a fuzzy match, as well as the translations of those subsegments.

In one embodiment, a method includes receiving an input vector corresponding to a query at a neural network model comprising a plurality of layers, wherein the plurality of layers comprise a last layer associated with a mapping matrix, generating a binary matrix based on the mapping matrix, an identity matrix, and one or more Gaussian vectors, generating an integer vector based on the binary matrix and a binary vector associated with the input vector, identifying a plurality of indices corresponding to a plurality of top values of the integer vector for the integer vector, generating an output vector based on the input vector and a plurality of rows of the mapping matrix, wherein the plurality of rows is associated with the plurality of identified indices, respectively, and determining the query is associated with one or more classes based on the output vector.

Provided is an artificial intelligence (AI) system which simulates the functions of a human brain, such as recognition, judgement, etc., by using a machine learning algorithm, such as deep learning, and applications thereof.

A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

Technical solutions relate to the natural language processing field based on artificial intelligence. According to an embodiment, a multilingual semantic representation model is trained using a plurality of training language materials represented in a plurality of languages respectively, such that the multilingual semantic representation model learns the semantic representation capability of each language; a corresponding mixed-language language material is generated for each of the plurality of training language materials, and the mixed-language language material includes language materials in at least two languages; and the multilingual semantic representation model is trained using each mixed-language language material and the corresponding training language material, such that the multilingual semantic representation model learns semantic alignment information of different languages.